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The Fabry-Perot sapphire optical fiber sensor is an excellent choice for high-temperature sensing in civil and military fields, such as oil exploitation, engine and turbine. The purpose of this paper is to study the high-reflective film system withstanding high temperature in Fabry-Perot sapphire optical fiber high-temperature sensor. To improve the performance of the sensor and reduce the difficulty of signal acquisition, one of the key ways is to enhance the normalized light intensity of F-P sensor, which can be achieved by coating the high-reflective film system on the fiber end.

The high-reflective film system can be achieved by a multilayer film with alternating ZrO₂ and Al₂O₃ film layers whose refractive indexes are different. In addition, the optimum film alternating sequences and the influence of the number of film layers, incident angle and temperature should be obtained by numerical analysis.

With the increase of the number of film layers, the reflectivity rises gradually and the change trend is more and more gentle. A minimum of the spectral reflectivity will occur at a certain incident angle depending on the design of the periodic multilayer system. Temperature affects the reflectivity of high-reflective film system. The normalized light intensity of the F-P sensor coated with high-reflective film system enhances greatly which is helpful to the signal demodulation. The temperature response of the F-P sensor is mainly determined by the characteristics of the F-P cavity.

Higher reflectivity, lower cost and easy signal acquisition are the most important features of the introduced high-reflective film system for the Fabry-Perot sapphire optical fiber high-temperature sensor.

This paper aims to propose a new ultrasonic diagnosis method for stainless steel weld defects based on multi-domain feature fusion to solve two problems in the ultrasonic diagnosis of austenitic stainless steel weld defects. These are insufficient feature extraction and subjective dependence of diagnosis model parameters.

To express the richness of the one-dimensional (1D) signal information, the 1D ultrasonic testing signal was derived to the two-dimensional (2D) time-frequency domain. Multi-scale depthwise separable convolution was also designed to optimize the MobileNetV3 network to obtain deep convolution feature information under different receptive fields. At the same time, the time/frequent-domain feature extraction of the defect signals was carried out based on statistical analysis. The defect sensitive features were screened out through visual analysis, and the defect feature set was constructed by cascading fusion with deep convolution feature information. To improve the adaptability and generalization of the diagnostic model, the authors designed and carried out research on the hyperparameter self-optimization of the diagnostic model based on the sparrow search strategy and constructed the optimal hyperparameter combination of the model. Finally, the performance of the ultrasonic diagnosis of stainless steel weld defects was improved comprehensively through the multi-domain feature characterization model of the defect data and diagnosis optimization model.

The experimental results show that the diagnostic accuracy of the lightweight diagnosis model constructed in this paper can reach 96.55% for the five types of stainless steel weld defects, including cracks, porosity, inclusion, lack of fusion and incomplete penetration. These can meet the needs of practical engineering applications.

This method provides a theoretical basis and technical reference for developing and applying intelligent, efficient and accurate ultrasonic defect diagnosis technology.

This study aims to address the challenge of the real-time state of charge (SOC) estimation for lithium-ion batteries in robotic systems, which is critical for monitoring remaining battery power, planning task execution, conserving energy and extending battery lifespan.

The authors introduced an optimal observer based on adaptive dynamic programming for online SOC estimation, leveraging a second-order resistor–capacitor model for the battery. The model parameters were determined by fitting an exponential function to the voltage response from pulse current discharges, and the observer's effectiveness was verified through extensive experimentation.

The proposed optimal observer demonstrated significant improvements in SOC estimation accuracy, robustness and real-time performance, outperforming traditional methods by minimizing estimation errors and eliminating the need for iterative steps in the adaptive critic and actor updates.

This study contributes a novel approach to SOC estimation using an optimal observer that optimizes the observer design by minimizing estimation errors. This method enhances the robustness of SOC estimation against observation errors and uncertainties in battery behavior, representing a significant advancement in battery management technology for robotic applications.

This paper aims to determine the perceptions of managers in China with respect to the pressures for, and the purposes of, social and environmental reporting (SER) in the Chinese context.

The authors interviewed 14 managers from nine different state-owned enterprises, all headquartered in Beijing. Interviews were conducted during 2009, the height of the period of growth in standalone corporate social responsibility (CSR) reporting in China. The authors assess the perceptions of the managers from a neo-institutional perspective.

The findings indicate that, similar to reporting in more Western economies, managers perceive that a variety of coercive, normative and mimetic pressures interplay to influence SER in the Chinese context. The managers perceive peer institutions as exerting the greatest pressures for reporting and, surprisingly, indicate that the government, rather than exerting coercive pressures for SER, instead is seen as playing a facilitating role. The findings also reveal that the managers almost uniformly see the purpose of the reporting as a tool of image enhancement, particularly with respect to the general public. However, in contrast to studies of organizational response to institutional change in other settings, the authors find little non-conformity.

The findings suggest neo-institutional arguments for CSR reporting appear to hold, even in China’s socialist market economy.

The finding that managers see CSR reporting as being largely about image enhancement may help to explain the low quality of disclosure documented in other studies.

Improving CSR disclosure in China would appear to require more mandated pressure from, particularly, governmental powers.

This is the first study to explore via in-depth interviews the perceptions of managers in China to the evolving practice of standalone CSR reporting.

The digital transformation in the banking industry has brought about complexity and competitiveness which has made differentiation challenging for banks. Complemented by consumer empowerment through high accessibility of information on the internet, this has led to a phenomenon known as switching behavior. The purpose of this review is to examine the determinants governing switching behavior among bank customers. This review highlights the importance of research which looks beyond pre-adoption behavior by examining post-adoption behavior; what happens after initial technology acceptance.

This review examines 44 journal articles researching switching behavior published between 1995 and 2022 in top journals. From a synthesis of literature, a conceptual framework for analysis and understanding switching behavior is presented.

Although various scholars have investigated switching behavior among bank customers, there are few studies which provide a comprehensive review and research classifications in this area. This review classifies key determinants of switching behavior into socio-demographic factors, situational triggers, influential triggers and reactional triggers. Structural equation modelling is the most common research methodology utilized in reviewed articles. The literature review reveals that mediators and moderators are less commonly deployed compared to determinants. Findings also indicate switching behavior studies still lack theory-driven conceptual frameworks.

This paper is the first systematic literature review on switching behavior research among bank customers spanning across 28 years in top academic journals. It integrates insights from 44 relevant research papers through publication trends. This review identifies key research gaps and provides future research directions.

The purpose of this study was to understand the impact of the coronavirus disease (COVID-19) pandemic on the global textile and apparel supply chain from the perspectives of industry professionals within the two leading production countries: Bangladesh and China.

Applying the theory of dynamic capabilities, a qualitative research design was employed using interviews with a total of 33 apparel industry practitioners, 15 in Bangladesh and 18 in China.

Findings indicate that four factors primarily impacted the extent to which COVID-19 disrupted a firm's supply chain: firm size, channel diversification, sourcing method and product type. Viewed through the lens of the theory of dynamic capabilities, findings point to the need for firms to address the challenges brought about by supply chain disruptions by creating opportunities.

It is important to understand the impact of COVID-19 in real time and within the two largest textile and apparel–producing countries globally, as they have borne much of the brunt of the supply chain disruptions brought on by COVID-19. This empirical study makes contributions to the apparel supply chain literature as it provides an in-depth investigation of what textile and apparel firms in China and Bangladesh have learned from the COVID-19 experience to better prepare for future unexpected global events.

The purpose of this paper is to examine translation in the context of International Financial Reporting Standards (IFRS) by taking the example of the English term “impairment” in IAS 36, and following it into 19 translations. The paper then examines the terms used for impairment in English translations of annual reports provided by firms. Consideration is given to the best approach for translating regulations and whether that is also suitable for the translation of annual reports.

The two empirical parts of the paper involve: first, identifying the terms for impairment used in 19 official translations of IAS 36, and second, examining English-language translations of reports provided by 393 listed firms from 11 major countries.

Nearly all the terms used for “impairment” in translations of IAS 36 do not convey the message of damage to assets. In annual reports translated into English, many terms are misleading in that they do not mention impairment, peaking at 39 per cent in German and Italian reports in one year.

Researchers should note that the information related to impairment in international databases is likely to contain errors, and the authors recommend that data should be hand-collected and then carefully checked by experts. The authors make suggestions for further research.

Translators of regulations should aim to convey the messages of the source documents, but translators of annual reports should not look only at the reports but also consult the terminology in the original regulations. The authors also suggest implications for regulators and analysts.

The paper innovates by separately considering regulations and annual reports. The authors examine a key accounting term systematically into a wide range of official translations. The core section of the paper is a new field of research: an empirical study of the translations of firms’ financial statements.

Snake-inspired robots are of great significance in many fields because of their great adaptability to the environment. This paper aims to systematically illustrate the research progress of snake-inspired robots according to their application environments. It classifies snake-inspired robots according to the numbers of degrees of freedom in each joint and briefly describes the modeling and control of snake-inspired robots. Finally, the application fields and future development trends of snake-inspired robots are analyzed and discussed.

This paper summarizes the research progress of snake-inspired robots and clarifies the requirements of snake-inspired robots for self-adaptive environments and multi-functional tasks. By equipping various sensors and tool modules, snake-inspired robots are developed from fixed-point operation in a single environment to autonomous operation in an amphibious environment. Finally, it is pointed out that snake-inspired robots will be developed in terms of rigid and flexible deformable structure, long endurance and multi-function and intelligent autonomous control.

Inspired by the modular and reconfigurable concepts of biological snakes, snake-inspired robots are well adapted to unknown and changing environments. Therefore, snake-inspired robots will be widely used in industrial, military, medical, post-disaster search and rescue applications. Snake-inspired robots have become a hot research topic in the field of bionic robots.

This paper summarizes the research status of snake-inspired robots, which facilitates the reader to be a comprehensive and systematic understanding of the research progress of snake-inspired robots. This helps the reader to gain inspiration from biological perspectives.

This review aims to give an overview about zinc oxide (ZnO) based gas sensors and the role of doping in enhancing the gas sensing properties. Gas sensors based on ZnO thin film are preferred for sensing applications because of their modifiable surface morphology, very large surface-to-volume ratio and superior stability due to better crystallinity. The gas detection mechanism involves surface reaction, in which the adsorption of gas molecules on the ZnO thin film affects its conductivity and reduces its electrical properties. One way to enhance the gas sensing properties is by doping ZnO with other elements. A few of the common and previously used dopants include tin (Sn), nickel (Ni) and gallium (Ga).

In this brief review, previous works on doped-ZnO formaldehyde sensing devices are presented and discussed.

Most devices provided good sensing performance with low detection limits. The reported operating temperatures were within the range of 200̊C –400̊C. The performance of the gas sensors can be improved by modifying their nanostructures and/or adding dopants.

As of yet, a specific review on formaldehyde gas sensors based on ZnO metal semiconductors has not been done.